This paper proposes a novel optimization algorithm, called Diffusion Algorithm Based on P Systems (DAPS), for continuous global optimization. This algorithm utilizes four different search strategies and a P system to explore the search space and find the global minima. These search strategies mimic the diffusion phenomena and a hybrid membrane structure inspired by chloroplasts and mitochondria is used as a distributed-parallel computing framework in the P system. The basic concepts and principles of the diffusion search strategies and the P system framework are explained in detail. For verifying the results of DAPS, comparisons with several well-known and recent metaheuristic optimization algorithms on 13 widely used benchmark functions are conducted. The experimental results demonstrate that DAPS is highly competitive and can provide better solution accuracy on the majority of the benchmark functions.

本文针对连续全局优化提出了一种新颖的优化算法，称为基于P系统的扩散算法（DAPS）。该算法利用四种不同的搜索策略和一个P系统来探索搜索空间并找到全局最小值。这些搜索策略模拟了扩散现象，并且受叶绿体和线粒体启发的混合膜结构被用作P系统中的分布式并行计算框架。详细解释了扩散搜索策略和P系统框架的基本概念和原理。为了验证DAPS的结果，在13种广泛使用的基准函数上与几种著名的和最近的元启发式优化算法进行了比较。